Journal of the Earthquake Engineering Society of Korea (한국지진공학회논문집)

Earthquake Engineering Society of Korea (한국지진공학회)
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Machine Learning-Based Allowable Axial Loading Estimation for RC Moment Frames

기계학습 기반 철근콘크리트 모멘트골조 축력허용범위 산정 방법

  • Hwang, Heejin (Department of Architectural Engineering, Gyeongsang National University) ;
  • Oh, Keunyeong (Department of Building Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Lee, Kihak (Department of Architectural Engineering, Sejong University) ;
  • Shin, Jiuk (Department of Architectural Engineering, Gyeongsang National University)
  • 황희진 (경상국립대학교 건축공학과) ;
  • 오근영 (한국건설기술연구원 건축연구본부 ) ;
  • 이기학 (세종대학교 건축공학과 ) ;
  • 신지욱 (경상국립대학교 건축공학과 )
  • Received : 2024.12.09
  • Accepted : 2025.02.17
  • Published : 2025.05.01
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Abstract

Seismically deficient reinforced concrete(RC) structures experience reduced structural capacity and lateral resistance due to the increased axial loads resulting from green retrofitting and vertical extensions. To ensure structural safety, traditional performance assessment methods are commonly employed. However, the complexity of these evaluations can act as a barrier to the application of green retrofitting and vertical extensions. This study proposes a methodology for rapidly calculating the allowable axial force range of RC buildings by leveraging simplified structural details and seismic wave information. The methodology includes three machine-learning-based models: (1) predicting column failure modes, (2) assessing seismic performance under current conditions, and (3) evaluating seismic performance under amplified mass conditions. A machine learning model was specifically developed to predict the seismic performance of an RC moment frame building using structural details, gravity loads, failure modes, and seismic wave data as input variables, with dynamic response-based seismic performance evaluations as output data. Classifiers developed using various machine learning methodologies were compared, and two optimal ensemble models were selected to effectively predict seismic performance for both current and increased mass scenarios.

Keywords

Acknowledgement

본 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원(RS-2024-00348713) 및 과학기술정보통신부의 재원으로 수행된 한국건설기술연구원 주요사업의 결과물임(No. 20240190-001).

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